The course "YARN MapReduce Architecture and Advanced Programming" provides an in-depth understanding of YARN and MapReduce architectures, focusing on their components and capabilities. Students will explore the MapReduce programming model and learn essential optimization techniques such as combiners, partitioners, and compression to improve job performance. The course covers Mapper and Reducer parallelism in MapReduce, along with practical steps for writing and configuring MapReduce jobs. Advanced topics such as multithreading, speculative execution, and input/output formats are also explored.
YARN MapReduce Architecture and Advanced Programming
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YARN MapReduce Architecture and Advanced Programming
This course is part of Big Data Processing Using Hadoop Specialization
Instructor: Karthik Shyamsunder
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
17 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Learn the fundamentals of YARN and MapReduce architectures, including how they work together to process large-scale data efficiently.
Understand and implement Mapper and Reducer parallelism in MapReduce jobs to improve data processing efficiency and scalability.
Apply optimization techniques such as combiners, partitioners, and compression to enhance the performance and I/O operations of MapReduce jobs.
Explore advanced concepts like multithreading, speculative execution, input/output formats, and how to avoid common MapReduce anti-patterns.
Skills you'll gain
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Recently updated!
January 2025
Assessments
12 assignments
Taught in English
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This course is part of the Big Data Processing Using Hadoop Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 5 modules in this course
This course provides a comprehensive introduction to YARN and MapReduce architectures, covering their fundamental components and capabilities. You will explore the MapReduce programming model, focusing on optimization techniques such as combiners, partitioners, and compression. Key concepts like Mapper and Reducer parallelism will be demonstrated, alongside practical steps for writing and configuring MapReduce jobs. The course also delves into advanced topics such as multithreading, speculative execution, and input/output formats. By the end, You will gain a deep understanding of MapReduce and be equipped to apply best practices in real-world scenarios.
What's included
2 readings
In this module, we will cover the architecture YARN architecture and architectural capabilities followed by MapReduce architecture built on YARN
What's included
6 videos4 readings3 assignments
This module provides a comprehensive overview of the MapReduce API, guiding you through the steps to write a MapReduce program. It covers the concepts of Mapper and Reducer parallelism, illustrating their implementation and impact on data processing efficiency.
What's included
6 videos5 readings3 assignments
This module focuses on advanced MapReduce optimization techniques, including the use of combiners to enhance performance, partitioners to manage data distribution across reducers, and compression methods to optimize I/O. It also covers the application of counters to collect and analyze statistics about MapReduce jobs.
What's included
6 videos5 readings3 assignments
This module explores advanced MapReduce concepts including multithreading, the internals of input/output formats, and speculative execution. It also covers running jobs locally and identifies common MapReduce anti-patterns to avoid.
What's included
7 videos5 readings3 assignments
Instructor
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Frequently asked questions
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